James H. Harrison

The development of health care data warehouses to support data mining.

Clinical data warehouses offer tremendous benefits as a foundation for data mining. By serving as a source for comprehensive clinical and demographic information on large patient populations, they streamline knowledge discovery efforts by providing standard and efficient mechanisms to replace time-consuming and expensive original data collection, organization, and processing. Building effective data warehouses requires knowledge of and attention to key issues in database design, data acquisition and processing, and data access and security. In this article, the authors provide an operational and technical definition of data warehouses, present examples of data mining projects enabled by existing data warehouses, and describe key issues and challenges related to warehouse development and implementation.

Temporal Data Mining

Large-scale clinical databases provide a detailed perspective on patient phenotype in disease and the characteristics of health care processes. Important information is often contained in the relationships between the values and timestamps of sequences of clinical data. The analysis of clinical time sequence data across entire patient populations may reveal data patterns that enable a more precise understanding of disease presentation, progression, and response to therapy, and thus could be of great value for clinical and translational research. Recent work suggests that the combination of temporal data mining methods with techniques from artificial intelligence research on knowledge-based temporal abstraction may enable the mining of clinically relevant temporal features from these previously problematic general clinical data.

Introduction to the Mining of Clinical Data

The increasing volume of medical data online, including laboratory data, represents a substantial resource that can provide a foundation for improved understanding of disease presentation, response to therapy, and health care delivery processes. Data mining supports these goals by providing a set of techniques designed to discover similarities and relationships between data elements in large data sets. Currently, medical data have several characteristics that increase the difficulty of applying these techniques, although there have been notable medical data mining successes. Future developments in integrated medical data repositories, standardized data representation, and guidelines for the appropriate research use of medical data will decrease the barriers to mining projects.

CARE Track for Advanced Cancer: Impact and Timing of an Outpatient Palliative Care Clinic.

BACKGROUND Studies suggest that outpatient palliative care can reduce hospitalization and increase hospice utilization for patients with cancer, however there are insufficient resources to provide palliative care to all patients from time of diagnosis. It is also unclear whether inpatient consultation alone provides similar benefits. A better understanding of the timing, setting, and impact of palliative care for patients with cancer is needed. OBJECTIVES The purpose of this study was to measure timing of referral to outpatient palliative care and impact on end-of-life (EOL) care. DESIGN The Comprehensive Assessment with Rapid Evaluation and Treatment (CARE Track) program is a phased intervention integrating outpatient palliative care into cancer care. In Year 1 patients were referred at the discretion of their oncologist. SETTING Academic medical center. MEASUREMENTS We compared EOL hospitalization, hospice utilization, and costs of care for CARE Track patients compared to those never seen by palliative care or seen only in hospital. RESULTS Patients were referred a median of 72.5 days prior to death. CARE Track patients had few hospitalizations at end of life, were less likely to die in hospital, had increased hospice utilization, and decreased costs of care; these results were significant even after controlling for differences between groups. Inpatient consultation alone did not impact these variables. However, only approximately half of patients with incurable cancers were referred to this program. CONCLUSION Referral outpatient palliative care within 3 months of death improved EOL care and reduced costs, benefits not seen with inpatient care only. However, many patients were never referred, and methods of systematically identifying appropriate patients are needed.

Cancer patient-reported outcomes assessment using wireless touch screen tablet computers

PurposeTo assess the feasibility of collecting patient-reported outcomes data with wireless touch screen tablet computers in the adult oncology palliative care setting.MethodsPatients were provided with tablet computers during scheduled clinic visits and answered online queries about their experience over the past week in the health domains of anxiety, depression, fatigue, pain interference, physical function, instrumental social support, sleep impairment, diarrhea, constipation, nausea, vomiting, anorexia, dyspnea, neuropathy, and spiritual values.ResultsContent analysis of patient interviews indicates that wireless touch screen tablet computers are a feasible approach for collecting patient-reported outcome measures by palliative care cancer patients presenting in clinic. Most patients indicated that the questionnaire was easy to answer. However, all but one patient requested some form of assistance, and many reported difficulties attributable to a lack of familiarity with the device, interpretation of certain questions, and wireless connectivity-related issues.ConclusionsThis feasibility study demonstrates that tablet computers have the potential to efficiently and reliably collect patient-reported health status measures among palliative care cancer patients presenting in clinics. The use of these devices may lead to substantial improvements by making patient-reported outcomes available for clinical decision-making.

Random Forests on Ubiquitous Data for Heart Failure 30-Day Readmissions Prediction

Heart failure is the most common reason for unplanned hospital readmissions. Typical 30 day readmission prediction models either use data that are not readily available at the majority of US hospitals or use modeling techniques that do not provide adequate prediction accuracy. Moreover, the tendency of ongoing studies is to incorporate clinical data that is only present in the most modern electronic health record systems (EHRs). This is problematic as the population most affected by heart disease, the rural poor, is also the same population whose hospitals have the slowest adoption rates of advanced EHR systems. We apply the machine learning technique random forests to administrative claims data to predict unplanned all-cause 30 day readmissions for congestive heart failure patients in a hospital system located in central Virginia, USA. We form two random forests model variants based on datasets comprised of procedure data, diagnosis data, a combination of both, and basic demographic data. Our results show significant predictive performance, yield importance rankings for candidate variables, and address heart failure readmissions in high-need areas.

Smartphones Can Monitor Medical Center Pneumatic Tube Systems

To the Editor: The pneumatic tube system (PTS) has become a common means of transportation of specimens in medical centers. Although the PTS provides convenience and speed of transport, hemolysis of blood specimens and preanalytical variation have been related to excessive acceleration forces and prolonged time/distance traveled in the PTS (1–5). As a result, regular assessment of 3-axis acceleration (i.e., forces) in PTSs has been recommended in an article in this journal (5). An editorial related to that article suggested that products designed for PTS assessment may become commercially available and capable of recording g -forces in PTSs (2). To date, however, we have found no products that are available in the US designed to record forces in the PTS used in our health system (Swisslog). Many modern smartphones are equipped with an accelerometer that measures acceleration forces. The devices also contain a chronometer, and they are nearly ubiquitous and are portable, …

Regional and National Health Care Data Repositories

Efforts are underway to define a national framework for secondary analysis of health-related data. In the meantime, regional health databases have been constructed using insurance claims data, clinical data from single large health care providers, clinical data from multiple collaborating health care providers, and public health data. Large-scale survey data also are available in government databases. Clinical laboratory results are an important component of all these databases because they can provide validation for manually assigned diagnostic and procedure codes and can support inference of key information not provided by coding, such as severity of disease and prevalence of risk factors.

Pathology informatics essentials for residents: A flexible informatics curriculum linked to accreditation council for graduate medical education milestones

Context: Recognition of the importance of informatics to the practice of pathology has surged. Training residents in pathology informatics have been a daunting task for most residency programs in the United States because faculty often lacks experience and training resources. Nevertheless, developing resident competence in informatics is essential for the future of pathology as a specialty. Objective: The objective of the study is to develop and deliver a pathology informatics curriculum and instructional framework that guides pathology residency programs in training residents in critical pathology informatics knowledge and skills and meets Accreditation Council for Graduate Medical Education Informatics Milestones. Design: The College of American Pathologists, Association of Pathology Chairs, and Association for Pathology Informatics formed a partnership and expert work group to identify critical pathology informatics training outcomes and to create a highly adaptable curriculum and instructional approach, supported by a multiyear change management strategy. Results: Pathology Informatics Essentials for Residents (PIER) is a rigorous approach for educating all pathology residents in important pathology informatics knowledge and skills. PIER includes an instructional resource guide and toolkit for incorporating informatics training into residency programs that vary in needs, size, settings, and resources. PIER is available at http://www.apcprods.org/PIER (accessed April 6, 2016). Conclusions: PIER is an important contribution to informatics training in pathology residency programs. PIER introduces pathology trainees to broadly useful informatics concepts and tools that are relevant to practice. PIER provides residency program directors with a means to implement a standardized informatics training curriculum, to adapt the approach to local program needs, and to evaluate resident performance and progress over time.

Smartphone monitoring of pneumatic tube system-induced sample hemolysis.

BACKGROUND Pneumatic tube systems (PTSs) are convenient methods of patient sample transport in medical centers, but excessive acceleration force and time/distance traveled in the PTS have been correlated with increased blood-sample hemolysis. We investigated the utility of smartphones for monitoring of PTS-related variables. METHODS Smartphones were sent through the PTS from several hospital locations. Each smartphone used 2 apps as data-loggers to record force of acceleration vs time. To relate the smartphone data to sample integrity, blood samples were collected from 5 volunteers, and hemolysis of the samples was analyzed after they were transported by hand or via 1 of 2 PTS routes. Increased sample hemolysis as measured by plasma lactate dehydrogenase (LD) was also related to the amount of transport in the PTS. RESULTS The smartphones showed higher duration of forceful acceleration during transport through 1 of the 2 PTS routes, and the increased duration correlated with significant increases in hemolysis (H)-index and plasma LD. In addition, plasma LD showed a positive linear relationship with number of shock forces experienced during transport through the PTS. CONCLUSIONS Smartphones can monitor PTS variables that cause sample hemolysis. This provides an accessible method for investigating specific PTS routes in medical centers.